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R-LAIR: Riverside Lab for Artificial Intelligence Research

Modeling ``Presentness'' of Electronic Health Record Data to Improve Patient State Estimation (2018)

by Jacob Fauber and Christian R. Shelton

Abstract: Medical data are not missing at random. The problem is more acute when the observations are over an extended period of time; any particular variable is observed at relatively few time points. We take missing values to be the norm, and treat ``presentness'' (the times of observations) as additional features to augment the values observed. A joint model over both avoids the missing-at-random (MAR) assumption. We use piecewise-constant conditional intensity models (PCIMs) to build a generative model of observation times and values. We demonstrate its effectiveness in reconstruction of monitor readings of patient vitals from sparse EHR data.

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Jacob Fauber and Christian R. Shelton (2018). "Modeling ``Presentness'' of Electronic Health Record Data to Improve Patient State Estimation." Proceedings of Machine Learning for Healthcare. pdf        

Bibtex citation

@inproceedings{FauShe18,
   author = "Jacob Fauber and Christian R. Shelton",
   title = "Modeling ``Presentness'' of Electronic Health Record Data to Improve Patient State Estimation",
   booktitle = "Proceedings of Machine Learning for Healthcare",
   booktitleabbr = "MLHC",
   year = 2018,
}

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